Robotic Search for Optimal Cell Culture in Regenerative Medicine

Abstract

Induced differentiation is one of the most experience- and skill-dependent processes in regenerative medicine, and establishing optimal conditions often takes years (an inordinate amount of time). Here, we developed a robotic-AI system that autonomously induces the differentiation of iPS cell-derived RPE (iPSC-RPE) cells. The system performed 216 forty-day cell culture experiments, with a total experimentation time of 8,640 days. The search for optimal differentiation conditions was accelerated using a novel batch Bayesian optimization technique with local penalization, compressing the search time to 185 days, with a cumulative robot operating time of 995 h. From 200 million possible parameter combinations, the system optimized the iPSC-RPE production conditions to yield improved pigmented scores that were up to 88% higher than the scores obtained with the pre-optimized conditions. Transferring tacit knowledge and skills often constitutes a serious obstacle when transposing basic cell experimental research from the laboratory to the medical forefront or for scaling to mass production. Our work demonstrates that autonomous robotic-AI systems can be effectively utilized for the systematic exploration of experimental conditions independently from the tacit knowledge of skilled professionals. This option guarantees immense use in future research.

Automated phenol-chloroform extraction of high molecular weight genomic DNA for use in long-read single-molecule sequencing

“…More notably, different tissue samples and organisms require different treatments to free the DNA from cells. Nevertheless, it is conceivable to use the platform to determine the optimal incubation for groups of samples of equivalent properties, in the same spirit as the work of Kanda et al (2020) which reported AI-based optimization of iPS cell-culture conditions with Maholo. Further improvements of the method, such as the use of wide-bore tips to reduce DNA shearing, require more extensive reprogramming of the LabDroid’s movements as it currently relies on a fixed shape for all its tools and consumables.…”

Nobel Turing Challenge: creating the engine for scientific discovery

“…exhaustively is not practical given the volume of publications that shall be processed by AI Scientist. Currently, certain types of experimental results are known to be difficult to reproduce 44 , where some aspects of reproducibility issues shall be reduced by automated, transparent, and traceable experimental systems [45][46][47] . Intrinsic variability of biological systems due to noise and individual variations are treated as intrinsic features 48,49 and shall be treated separately from ambiguities and inaccuracy caused by the process of research itself.…”

“…. penalization 47 . Optimization of lycopene biosynthesis pathway and biofuels for synthetic biology-based bio-manufacturing are other examples automated search of design space was shown to be effective 61,62 .…”

Optimal Scheduling for Laboratory Automation of Life Science Experiments with Time Constraints

“…On the other hand, multipurpose instruments (e.g., liquid handling workstations and dual-arm humanoid robots) have also been actively developed because they offer the flexibility to carry out various kinds of experiments. [13][14][15][16] However, despite the development of these laboratory automation instruments, one generally needs to use several different types of instruments to execute even one procedure composed of multiple steps 17,18 because a single instrument is rarely capable of performing all the steps in the procedure.…”